Gary Sean Escola, MD, PhD

Profile Headshot

Overview

Academic Appointments

  • Assistant Professor of Psychiatry at CUMC

Gender

  • Male

Credentials & Experience

Education & Training

  • MD, 2010 Columbia University College of Physicians and Surgeons
  • Residency: NewYork-Presbyterian Hospital/Columbia University Medical Center

Research

Behaviors and cognitions arise from an interplay between neural activity driven by external stimuli and internal activity patterns or “brain states” that reflect motivation, intention, and experience. Historically, neuroscience research has focused almost exclusively on stimulus-driven activity, ignoring the impact of internal state. Clearly, to develop robust computational theories of cognitive function and to understand how computations may fail in psychiatric disease, we must develop a new neuroscience that measures and models both externally and internally generated neural activity and seeks to reveal the interactions between them. On the data side, we must be able to detect transitions between internal states and analyze stimulus-evoked responses within the context of the state in which they occur. On the theory side, we must develop models for how internal states are generated, changed during behavior, and affect sensory responses. These are the goals of my research.

Research Interests

  • Cognitive/Systems Neuroscience
  • Computational Psychiatry
  • Computation and Theory
  • Motor Systems
  • Theoretical Neuroscience

Grants

THE INTERNAL STATES OF NEURAL CIRCUITS: DATA ANALYSIS, MODELING AND DISEASE (Federal Gov)

Sep 18 2014 - Aug 31 2019

THE INTERNAL STATES OF NEURAL CIRCUITS: DATA ANALYSIS, MODELING AND DISEASE (Federal Gov)

Sep 18 2014 - Aug 31 2019

Selected Publications

  • Escola S, Fontanini A, Katz D, and Paninski L. Hidden Markov models for the stimulus-response relationships of multi-state neural systems. Neural Computation, 2011 May;23(5):1071-132.
  • Escola S, Eisele M, Miller K, and Paninski L. Maximally reliable Markov chains under energy constraints. Neural Computation, 2009 Jul;21(7):1863-912.